1,721,007 research outputs found
Quantum Techniques in Machine Learning
No full textIn the last few years, we have witnessed an increasing interest in bridging two impor-
tant research areas that fundamentally changed our way and abilities of processing
information, namely Machine Learning and Quantum Computation.
In the Summer 2017, we had the idea of inviting major experts in Quantum Compu-
tation and Information on the one hand, and in Machine Learning and Optimisation
on the other hand, for a meeting at the University of Verona to discuss the latest
advances in the newly born field of Quantum Machine Learning.
The idea developed in a very successful workshop, bringing together more than hun-
dred scientists to attend and/or contribute their results on the two-way interaction
between Machine Learning and Quantum Computation, aimed at demonstrating
how the intersection of the two fields can offer great potential for both.
This special issue is dedicated to this event, which was held in Verona on
6-8 November 2017 under the name of QTML 2017 - 1st Workshop on Quantum
Techniques in Machine Learning. It represents the first of a series of workshop that
are now held yearly in diverse places worldwide.
The volume collects original contributions focused on the following topics and not
limited to the works presented at QTML 2017:
- Quantum computing for enhancing machine learning algorithms
- Machine learning techniques for the analysis of interacting quantum systems
- Quantum entanglement and topology for the efficient representation of quan-
tum systems
- Approaches to machine learning based on Topological Quantum Computation
- Algorithmic techniques for quantum optimisation (e.g. quantum annealing).
We wish to thank all the people who contributed to bringing this special issue
to completion. In particular, we are very grateful to the reviewers who provided a
valuable help to ensure the high quality of the papers, to the authors for the scrupu-
lous work in improving their papers following the reviewers' suggestions, and to all
the participants in QTML 2017 whose lively discussions and critical interventions
greatly inspired the authors
Quantum computers,Computer quantistici
No full textA cosa serve un computer quantistico e come si puo' realizzare fisicament
On Quantitative Analysis of Probabilistic Protocols
No full textWe advocate the use of approximate noninterference for the security analysis of probabilistic protocols. Our approach relies on a formalisation of the protocol in the setting of a probabilistic process algebra and a notion of process similarity based on weak probabilistic bisimulation. We illustrate this approach by presenting the analysis of a probabilistic nonrepudiation protocol which allows us to quantitatively estimate its fairness degree
Molecular junctions enhancing thermal transport within graphene polymer nanocomposite: A molecular dynamics study
Full text linkThermal conductivity of polymer-based (nano)composites is typically limited by thermal resistances occurring at the interfaces between the polymer matrix and the conductive particles as well as between particles themselves. In this work, the adoption of molecular junctions between thermally conductive graphene foils is addressed, aiming at the reduction of the thermal boundary resistance and eventually lead to an efficient percolation network within the polymer nanocomposite. This system was computationally investigated at the atomistic scale, using classical Molecular Dynamics, applied the first time to the investigation of heat transfer trough molecular junctions within a realistic environment for a polymer nanocomposite. A series of Molecular Dynamics simulations were conducted to investigate the thermal transport efficiency of molecular junctions in polymer tight contact, to quantify the contribution of molecular junctions when graphene and the molecular junctions are surrounded by polydimethylsiloxane (PDMS) molecules. A strong dependence of the thermal conductance was found in PDMS/graphene model, with best performances obtained with short and conformationally rigid molecular junctions. Furthermore, the adoption of the molecular linkers was found to contribute additionally to the thermal transport provided by the surrounding polymer matrix, demonstrating the possibility of exploiting molecular junctions in composite materials
Studio per l’individuazione delle cultivar di ciliegio dolce più valide per la provincia di Bari.
No full textComportamento in vivaio di 40 cultivar di ciliegio dolce (Prunus avium L.) innestate su magaleppo (Prunus mahaleb Mill
No full textEstimating the Maximum Information Leakage
Full text linkPreventing improper information leaks is a greatest challenge of the
modern society. In this paper we present a technique for measuring the
ability of several families of adversaries to set up a covert channel.
Our approach relies on a noninterference formulation of security which
can be naturally expressed by semantic models of program execution.
In our analysis the most powerful adversary is measured via a notion
of approximate process equivalence. Even if finding the most powerful
adversary is in general impractical, we show that this requires only a
finite number of checks for a particular family of adversaries which
are related to a probabilistic information flow property
Noninterference and the Most Powerful Probabilistic Adversary
No full textProbabilistic noninterference extends the classical possibilistic notion introduced by Goguen and Meseguer in order to capture the information leakage caused by adversaries that set up probabilistic covert channels. In this setting we investigate how to evaluate the observational power of an adversary to the purpose of establishing the maximal security degree of a given system. We introduce three classes of probabilistic adversaries, which represent the different observational power of an adversary, and then we establish properties for each such classes which state the complexity of effectively computing the most powerful adversary
PERSISTENT HOMOLOGY ANALYSIS OF MULTIQUBIT ENTANGLEMENT
Full text linkWe introduce a homology-based technique for the classification of multiqubit state vectors with genuine entanglement. In our approach, we associate state vectors to data sets by introducing a metric-like measure in terms of bipartite entanglement, and investigate the persistence of homologies at different scales. This leads to a novel classification of multiqubit entanglement. The relative occurrence frequency of various classes of entangled states is also shown
A Quantitative Approach to Noninterference for Probabilistic Systems
No full textWe present a technique for measuring the security of a system which relies on a probabilistic process algebraic formalisation of noninterference. We define a mathematical model for this technique which consists of a linear space of processes and linear transformations on them. In this model the measured quantity corresponds to the norm of a suitably defined linear operator associated to the system. The probabilistic model we adopt is reactive in the sense that processes can react to the environment with a probabilistic choice on a set of inputs; it is also generative in the sense that outputs autonomously chosen by the system are governed by a probability distribution. In this setting, noninterference is formulated in terms of a probabilistic notion of weak bisimulation. We show how the probabilistic information in this notion can be used to estimate the maximal information leakage, i.e. the security degree of a system against a most powerful attacker
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